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description: How to get useful incident metrics.
sidebar_position: 1
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# Incidents

## Aggregated top-line metrics

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![](/img/admin-ui-dashboard-top-line.png)

</div>

These metrics are aggregated across all currently filtered incidents.

## Breakdown on key incident facets

### By Incident Type

<div style={{textAlign: 'center'}}>

![](/img/admin-ui-dashboard-type.png)

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### By Incident Priority

<div style={{textAlign: 'center'}}>

![](/img/admin-ui-dashboard-priority.png)

</div>

## Forecasting

Dispatch has the ability to do some _simple_ forecasting. It looks at prior incident history and applies [Exponential Smoothing](https://machinelearningmastery.com/exponential-smoothing-for-time-series-forecasting-in-python/#:~:text=Exponential%20smoothing%20is%20a%20time%20series%20forecasting%20method%20for%20univariate%20data.&text=Exponential%20smoothing%20forecasting%20methods%20are,decreasing%20weight%20for%20past%20observations) to guess how many incidents will be encountered in the future.

This works okay for small incident loads but becomes better with more incidents. If there isn't enough data to make a reasonable forecast one will not be displayed in the UI.

An example forecast:

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![](/img/admin-ui-dashboard-forecast.png)

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